We've disabled the flower visualization while we work on making it more useful
for exploring your data. Check back in future versions for a new, improved flower!

The correlation visualization is disabled because there are fewer than two streams visible.

To enable the correlation visualization, make at least two streams visible.

Converts each data point to a rolling average (an average of the previous data points).
Good for seeing underlying trends in noisy data.

The rows tool divides up your dataset according to its value – high, medium, or low, etc.
From here you can change the colors or thresholds that are used for the Flower View
.

The time shift tool visualizes your data with a time offset.

The Y-shift tool adjusts how the y-axis is displayed. It does not change any values.
Dragging the arrows will stretch and shrink your data. Dragging the middle of the bar
moves the whole line up and down.

Lets you zoom using your scroll wheel to a particular data point. Point your cursor to
the data of interest and scroll. Clicking in the space between the handle bars will fully
zoom you out.

The zoom you choose applies across all visualizations. That means that if you are only
viewing two months on the time series, and click over to periodic pattern
, only two months’ worth of data will be included.

Chooses how far back in time to go to include previous data points in the rolling average.

Quantiles

Divides up your data into equal parts. For example, if I had 100 data points, and I selected quartiles,
the lowest 25 data points would be in the first quartile, the next lowest 25 in the second, etc.

Equal Ranges

Divides up the Y-axis (where the values are) into equal parts. If your lowest data point was a 1,
and the highest 100, the first row would be 1-25, the second 25-50, etc.

Whiskers show (from bottom to top) the minimum, third quartile, median, first quartile, and maximum for data in that time period.

Absolute scaling is currently turned on. Data in each time period is scaled relative to data from the other streams, across all time periods.

Absolute scaling is currently turned off. Data in each time period is scaled relative to data from the stream itself, across all time periods.

See and search annotations.

Exported data will match what is displayed in the Timeseries visualization, and will reflect changes that were applied there. These changes may include rolling average, offsets, and so on.

These are the rules that Data Sense uses to create the
Periodic Pattern view
and Flower view .
By default, we show the average of each data point that occurs in that time slot (bin).

To make your own workweeks vs weekends, commute hours, etc. click “New” in the Time Bins
dropdown. Each time slot (Monday, Tuesday, etc.) is a “bin”.

Changes the value Data Sense uses to determine high medium or low, etc.
Slide the black dot up or down to your desired level. From here you can also control what
color is used for each level. For more precision, use the rows tool
in Time Series.

Menu lets you change the times shown. Clicking on “Time 1” displays the labels directly on the flower.

Menu lets you change the times shown. Clicking on “Time 2” displays the labels directly on the flower.

Shows data as map pins. In this mode, one or more streams can be shown at once.
Black pins represent locations for which data from more than one stream is visible.

Shows data as circular orbs. In this mode, only one stream is shown at once,
and the orb size reflects the values for that stream at that location.

Draws a box around data to create a new subset.

+1 means this always goes up with that, -1 means this always goes down when that goes up.

Lets you change how your data is grouped before being correlated.

In general, you are more likely to get a better indication of whether there is a correlation if you correlate by hour or day than by week or month.

Data Sense has to group data together when sample rates are not the same.
If steps are collected every minute, but mood is collected once a day, then it can only correlate by day.

Some data, like steps, should be summed to make a total for the day, while others, like mood, should be averaged across the day.
Only you really know what is most appropriate for your data.

In general, you are more likely to get a better indication of whether there is a correlation if you correlate by hour or day than by week or month.

Correlations could not be computed for the currently selected timespan.

To see correlations, select a different timespan using the “Correlate by” selector below, and/or
change the correlation settings for the streams in this experiment using each stream’s menu.

Making your data available for anonymous aggregation pools data with other users, allowing others (and you) to make anonymous queries.
Any time your data is queried, Data Sense gives you back the results, so you can see how others are using your data.
Learn how we protect your privacy in this feature here.

Create social experiments by aggregating data that other users have shared.

Find Data Builder templates that you can use with your data.

The feed is where Data Sense keeps all your stuff.
These boxes filter what you see.
For now, uncheck the others to leave just “Sources.”

Hover your mouse over it to reveal the options pane, then click “Details” to see the data streams that it contains.

One is a data stream of caloric intake.
Click on “Calories” to see details about this individual stream.

Here we can see a preview of the data in this stream, as well as edit its name, add notes, or change how it is being used and shared.
To process this data further, we’ll need to start an experiment, so click the left arrow to return to the Data Sense Feed.

Experiments give you a variety of ways to process, analyze and compare your data.
Click the green button above to start a new one.

Choose “Look at my data” to begin the new experiment.

Click the green button to add data streams to this experiment.

The list shows streams from all available source files.
You can search and filter it above.
Choose the “Calories”stream from our sample source file and click on it to see details.

Here we can see the source file that contains this stream, as well as the type of data it contains.
Click “Add” to put this stream into our new experiment, then click “Done.”

By default, Data Sense shows the entire data set.
To zoom in and see more detail, drag the gray timeline bracket until you can see individual points.

These controls let you view data in different ways.
Let’s switch to Periodic Pattern mode to see if it reveals any trends in the data.

We can see the data in a new way: grouped by the time of day it was collected.
Now click on the down arrow to view details about this experiment.

Here you can also add notes about the experiment, and change who can view it.
Give your experiment a name to help you remember what the data shows

Data Sense saves your work automatically.
You can click on “Data Sense”at any time to exit the experiment and return to the feed.

You can return to this tour or learn more about Data Sense by clicking on the “Learn”tab above.
Enjoy using Data Sense!

Drag your sources to the area above to work with them. You can use as
many sources as you want, then whittle and crunch them with the other
operators.

Include or exclude data, based on some criteria. Start with Filter
Stream below, then connect a filter at top and a stream to be filtered at left.

Crunch your data by transforming and combining it to answer your questions.
Use Line Up Data or Time Shift to help line up data
that isn't at exactly the same time.

Output your data for use elsewhere in Data Sense.

Run a calculation over many sources of data at once.

Whittles a specified stream down to only the data included by a specified filter.

Whittles two streams down using a two-stream filter. For instance, selecting an
area in the correlations visualization makes a two-stream filter, one that includes
data in the selected area.

Defines how filters are put together.

Includes data only if it meets criteria in every filter attached.

Includes data only if it meets criteria in any filter attached.

Includes data only if it does not meet the attached filter's criteria.

Includes data based on time.

Includes data before or after a specified date.

Includes data during "bins" (weekends, commute hours, etc.) you previously set in an
experiment as part of a time cluster.

Includes data during a specified time range.

Includes data based on selections you previously made in an experiment.
To use these, make selections in your experiments, then click "View in Builder".

A selection of one or more time bins from the periodic patterns visualization.

A selection of one or more flower parts from the flower visualization.

An area selection from the map visualization.

An area selection from the correlations visualization.

Includes data that starts with, contains, or ends with a specified search term.

Includes data based on values.

Includes data higher or lower than a specified value.

Includes data with the specified value. You can use this with both numeric
and textual data.

Includes data in a specified value range.

Includes data that matches a specified value or set of values.

Apply arithmetic (+ - × ÷) on streams and/or numbers.
Arithmetic on two or more streams is applied to the values that line up in time.
If much of your data did not occur at exactly
the same time, use Line Up Data to make them both hourly, daily, etc.

Adds data from two or more sources.

Subtracts the data connected at the bottom from the data connected at the top.

Multiplies data from two or more sources.

Divides the data connected at the top by the data connected at the bottom.